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Approach to the Patient with Short Stature: Genetic Testing
No full textThe first step in the evaluation of the short child is to decide whether growth parameters in the context of the history are abnormal or a variant of normal. If growth is considered abnormal, system and hormonal tests are likely to be required, followed by more directed testing, such as skeletal survey and/or genetic screening with karyotype or microarray. In a small percentage of short children in whom a diagnosis has not been reached, this will need to be followed by detailed genetic analysis; currently exome sequencing using targeted panels relevant to the phenotype is the commonly used test. Clinical scenarios are presented that illustrate how such genetic testing can be used to establish a molecular diagnosis, and how that diagnosis contributes to the management of the short child. New genetic causes for short stature are being recognised on a frequent basis, while the clinical spectrum for known genes is being extended. We recommend that an international repository for short stature conditions is established for new findings to aid dissemination of knowledge, but also to help in the definition of the clinical spectrum both for new and established conditions
Enhanced biosynthesis of Polyhydroxyalkanoates by continuous feeding of volatile fatty acids in Haloferax mediterranei
No full textIn this work, the biosynthesis of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) in Haloferax mediterranei was enhanced by continuous feeding of volatile fatty acids. Using this strategy, polymer production was doubled to around 5 g L–1 PHBV when compared to pulse-fed fed-batch fermentations. Polymer productivity and yield increased up to 12.8 mg L–1 h–1 and 0.63 g g–1 respectively when the carbon concentration in the fermentation media was kept constant at 0.25 molar. This biopolymer production was achieved in less than half the time when compared to pulse feeding, effectively quadrupling the overall PHA productivity. Control over co-polymer composition was achieved and maintained at around 40 mol% 3-hydroxyvalerate (3HV). A correlation between substrate consumption and cell growth was observed, providing a crucial tool for feeding rate selection in future fermentations. The higher productivity and yield obtained with the novel feeding strategy will be key to future industrial scale PHA production
Self-assembled 1T-MoS2/Functionalized Graphene Composite Electrodes for Supercapacitor Devices
No full textTwo-dimensional (2D) materials such as graphene and molybdenum disulfide (MoS2) have been investigated widely for applications in energy storage, including supercapacitors, due to their high specific surface area, potential redox activity and mechanical flexibility. However, electrodes comprised of either pure graphene and MoS2 have failed to reach their potential due to restacking of the layered structure and poor electrical conductivity. It has been shown previously that composite electrodes made from a mixture of graphene and MoS2 partially counteract these issues, however performance is still limited by poor mixing at the nanoscale. Herein, we form a true composite electrode by chemically functionalizing the graphene so that the negatively charged surface can self-assemble with the positively charged 1T-MoS2 to give an alternating layer structure. These alternately restacked 2D materials were then used to produce supercapacitor electrodes, and their energy storage properties characterized. This stacked structure has increased the interlayer spacing of 1T-MoS2 which was indicated by the increase of the intensity of the (001) peak in the XRD spectra. Furthermore, the typically metastable 1T-MoS2 was stabilized by the interaction with the functionalized graphene, preventing it reverting back to the 2H phase, which was observed when pristine graphene was used. The graphene was functionalized using either 4-bromobenzenediazonium (BBD) or 4-nitrobenzenediazonium (NBD), with the later giving optimal capacitance when mixed with the MoS2. The alternative layer graphene-MoS2 structure was confirmed by Raman spectroscopy and electron microscopy and lead to high specific capacitance (290 F cm-3 at 0.5 A g-1) and 90 % retention of capacitance after 10,000 cycles
Obstetric Outcomes in Women with Rheumatic Disease and COVID-19 in the Context of Vaccination Status
No full textObjective:To describe obstetric outcomes based on COVID-19 vaccination status, in women with rheumatic and musculoskeletal diseases (RMDs) who developed COVID-19 during pregnancy. Methods:Data regarding pregnant women entered into the COVID-19 Global Rheumatology Alliance registry from 24 March 2020 to 25 February 2022 were analysed. Obstetric outcomes were stratified by number of COVID-19 vaccine doses received prior to COVID-19 infection in pregnancy. Descriptive differences between groups were tested using the chi -square or Fisher’s exact test. Results: There were 73 pregnancies in 73 women with RMD and COVID-19. Overall, 24.7% (18) of pregnancies were ongoing, while of the 55 completed pregnancies 90.9% (50) of pregnancies resulted in livebirths. At the time of COVID-19 diagnosis, 60.3% (n=44) of women were unvaccinated, 4.1% (n=3) had received one vaccine dose while 35.6% (n=26) had two or more doses. Although 83.6% (n=61) of women required no treatment for COVID-19, 20.5% (n=15) required hospital admission. COVID-19 resulted in delivery in 6.8% (n=3) of unvaccinated women and 3.8% (n=1) of fully vaccinated women. There was a greater number of preterm births (PTB) in unvaccinated women compared to fully vaccinated 29.5% (n=13) vs 18.2%(n=2). Conclusion:In this descriptive study, unvaccinated pregnant women with RMD and COVID-19 had a greater number of PTB compared with those fully vaccinated against COVID-19. Additionally, the need for COVID-19 pharmacological treatment was uncommon in pregnant women with RMD regardless of vaccination status. These results support active promotion of COVID-19 vaccination in women with RMD who are pregnant or planning a pregnancy.<br/
Comparison of biomedical relationship extraction methods and models for knowledge graph creation
No full textBiomedical research is growing at such an exponential pace that scientists, researchers, and practitioners are no more able to cope with the amount of published literature in the domain. The knowledge presented in the literature needs to be systematized in such a way that claims and hypotheses can be easily found, accessed, and validated. Knowledge graphs can provide such a framework for semantic knowledge representation from literature. However, in order to build a knowledge graph, it is necessary to extract knowledge as relationships between biomedical entities and normalize both entities and relationship types. In this paper, we present and compare a few rule-based and machine learning-based (Naive Bayes, Random Forests as examples of traditional machine learning methods and DistilBERT, PubMedBERT, T5, and SciFive-based models as examples of modern deep learning transformers) methods for scalable relationship extraction from biomedical literature, and for the integration into the knowledge graphs. We examine how resilient are these various methods to unbalanced and fairly small datasets. Our experiments show that transformer-based models handle well both small (due to pre-training on a large dataset) and unbalanced datasets. The best performing model was the PubMedBERT-based model fine-tuned on balanced data, with a reported F1-score of 0.92. The distilBERT-based model followed with an F1-score of 0.89, performing faster and with lower resource requirements. BERT-based models performed better than T5-based generative models
A Benchmark for Multi-Class Object Counting and Size Estimation Using Deep Convolutional Neural Networks
No full textAutomatic object counting and object size estimation in digital images can be very useful in many real-world applications such as surveillance, smart farming, intelligent traffic systems, etc. However, most existing research mainly focus on scenarios where only one type of object is considered due to the lack of proper datasets. Furthermore, they use the traditional detection algorithms for size estimation and can only do segmenting tasks but cannot identify different types of objects and return corresponding individual size information. To fill these gaps, we create a synthetic dataset and propose a benchmark for multi-class object counting and size estimation (MOCSE) within a unified framework. We create the dataset MOCSE13 by using Unity to generate synthetic images for 13 different objects (fruits and vegetables). Besides, we propose a deep architecture approach for multi-class object counting and object size estimation. Our proposed models with different backbones are evaluated on the synthetic dataset. The experimental results provide a benchmark for multi-class object counting and size estimation and the synthetic dataset can be served as a proper testbed for future studies
Theoretical study of the stability of the tetradymite-like phases of Sb2S3, Bi2S3, and Sb2Se3
No full textWe report a comparative theoretical study of the Pnma and R-3m phases of Sb2S3, Bi2S3, and Sb2Se3 close to ambient pressure. Our enthalpy calculations at 0 K show that at ambient pressure the R-3m (tetradymite-like) phase of Sb2Se3 is energetically more stable than the Pnma phase, contrary to what is observed for Sb2S3 and Bi2S3, and irrespective of the exchange-correlation functional employed in the calculations. The result for Sb2Se3 is in contradiction to experiments where all three compounds are usually grown in the Pnma phase. This result is further confirmed by free-energy calculations taking into account the temperature dependence of the unit-cell volumes and phonon frequencies. Lattice dynamics and elastic tensor calculations further show that both Pnma and R-3m phases of Sb2Se3 are dynamically and mechanically stable at zero applied pressure. Since these results suggest that the formation of the R-3m phase for Sb2Se3 should be feasible at close to ambient conditions, we provide a theoretical crystal structure and simulated Raman and infrared spectra to help in its identification. We also discuss the results of the two published works that have claimed to have synthesized tetradymite-like Sb2Se3. Finally, the stability of the R-3m phase across the three group-15 A2X3 sesquichalcogenides is analysed based on their van der Waals gap and X-X in-plane geometry
An online data driven fault diagnosis and thermal runaway early warning for electric vehicle batteries
No full textBattery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods for early failure detection and reducing safety risks from failing high energy lithium-ion batteries has become a major challenge for industry. In this work, a real-time early fault diagnosis scheme for lithiumion batteries is proposed. By applying both the discrete Fréchet distance (DFD) and local outlier factor (LOF) to the voltage and temperature data of the battery cell/module that measured in real time, the battery cell that will have thermal runaway is detected before thermal runaway happens. Compared with the widely used single parameter based diagnosis approach, the proposed one considerably improve the reliability of the fault diagnosis and reduce the false diagnosis rate. The effectiveness of the proposed method is validated with the operational data from electric vehicles with/without thermal runaway in daily use
On Local Input-Output Stability of Nonlinear Feedback Systems via Local Graph Separation
No full textA new type of local input-output stability for nonlinear systems is defined, called M-local boundedness, which can be viewed as a local version of established definitions of global boundedness. This definition states that the system is bounded if the input Lebesgue signal has a norm smaller than M. Using graph separation concepts and a novel topological argument, which partitions the output space of the system into feasible and infeasible regions based on the restriction of the system input, sufficient conditions for M-local boundedness of a nonlinear feedback system are derived. Using this theorem, a new local nonlinear small gain condition is found for a closed-loop system with additive inputs. This small gain condition is then used in a numerical example, in which a differential equation with a quadratic element was partitioned into a feedback system and bounds on the norm of the input were found which ensured the system was M-locally stable